Many businesses in the service and manufacturing sectors that deal with customers emphasize the importance of improving customer care. However, there is competing pressure to reduce the cost of such services. There is a growing trend to move customer care from human operators to automated call centers when a customer is using a telephone channel. However with the ubiquitous availability of networked computing devices which have more bandwidth than conventional telephone channels, there is a great opportunity to provide better customer experience with rich and pertinent content embedded in e-mails or other text-based customer care messages. Also, providing text-based customer care messages can significantly reduce the overall cost of customer care services since it allows for asynchronous interaction with customers permitting for multitasking on the part of the customer care agent.
One contemporary approach to responding to customer text-based natural language messages, such as e-mails, instant messages or other text messages, involves a customer care agent reading the e-mails and preparing individual responses from scratch to address the issues raised in the incoming customer's e-mail. As these messages are natural language messages, generally written in some form of prose or other form of contemporary written communication, they are typically analyzed by a human rather than a machine. This approach does not take advantage of similar messages to which responses were previously generated and transmitted to the customer.
Another approach employs a database of pre-formed boilerplate response messages that the agent can edit in order to more fully address the customer's issues before sending it as a formal response. This approach can expedite the process for the customer care agent, but often involves rewriting most of the preformed template or cutting and pasting a number of such template responses. Such an approach is still labor intensive and promotes inconsistencies between responses of different customer care agents generating them.
There is therefore a need for an efficiency enhancing system for generating responses to customer text-based natural language messages. Such a system preferably takes advantage of a collection of prior customer e-mails and corresponding agent responses. The collection is segmented, parsed and indexed such that incoming customer e-mails can be similarly analyzed and a new response automatically generated.